BRIDGING THE GAP: IDENTIFYING AND ADDRESSING BARRIERS TO CARE FOR PATIENTS WITH LUPUS AND LUPUS NEPHRITIS
Bibliographic record
Abstract
PV082 / #381 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Systemic Lupus Erythematosus (SLE) and Lupus Nephritis (LN) present significant healthcare challenges, particularly in terms of access to care and patient education. This project aims to identify and mitigate these barriers through a needs assessment and provision of supportive services to disadvantaged/underserved patients. The work presented here describes the electronic health record (EHR)-documented healthcare barriers identified among patients with lupus at a large academic healthcare system. Methods Patients with lupus were identified through the Carolina Data Warehouse for Health (CDW-H), an EHR data repository for patients who have been admitted to the University of North Carolina (UNC) healthcare system. Inclusion Criteria: Eligible patients were adults aged 18 or older, fluent in English, diagnosed with lupus nephritis, and receiving care at UNC Rheumatology and/or Nephrology clinics. Results A total of 1,673 unique patients with various SLE diagnoses were identified, resulting in 11,890 clinical encounters between July 1, 2020, and July 1, 2024. The majority of encounters were in rheumatology (64%) compared to nephrology (36%). Insurance coverage varied, with 52% of patients having other commercial or state health plans, 39% on Medicare, 27% on Medicaid, and 12% with no recorded insurance (Table 1). A total of 803 (48%) patients had recorded responses to Social Determinants of Health (SDOH) questionnaires available in the EHR. Of these patients, 8.2% experienced transportation barriers, 16.3% reported indicators of food insecurity, and 15.4% reported financial instability (Table 2). Table 1. Patient Insurance Status Recorded in the Electronic Health Record (n=1673 patients with lupus) Table 2. Social Determinants of Health (SDOH) Measures Recorded in the Electronic Health Record (n=803, 46% of patients with lupus) Conclusions A number of barriers and priorities to address in assistance programs were identified, including a relatively high number of patients from low socioeconomic status (39% of patients reporting Medicaid or no insurance), and more than 15% of patients reporting financial difficulty meeting their basic needs. This ongoing work aims to bridge the gap in healthcare access and education for patients with SLE and LN, leveraging community resources and targeted interventions to improve patient outcomes. Our goal is to enhance support services and education for patients with lupus by: 1) Improving access to healthcare transportation through rideshare vouchers; 2) Providing pharmacist counseling on medication adherence, adverse effects, and assistance programs; and 3) Distributing educational materials to increase patient knowledge and perception of lupus and access to care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".